4 Raspberry Pi 5 running large models

Post author:omagine
Post published:2025-03-30
Post category:News
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“Run Large Models Effortlessly with a 4-Node Raspberry Pi 5 Cluster – This Might Be the Most Mind-Blowing Open-Source AI Project of 2025!”
GitHub star project distributed-llama unveils its latest real-world case: Through its innovative dynamic model slicing technology, the team successfully ran the DeepSeek R1 Distill 8B model on 4 Raspberry Pi 5 devices (8GB RAM), achieving an inference speed of 6.43 tokens/s with a power consumption of just 20W! This article dives deep into:

✅ Core technical architecture of Raspberry Pi clusters
✅ Zero-threshold deployment workflows
✅ Community-tested performance benchmarks

Plus, a Raspberry Pi-specific configuration template at the end to turn your old devices into AI compute nodes!

Project Background

DeepSeek R1

distributed-llama is an open-source initiative launched by developer Bartłomiej Tadych, aiming to transform household idle devices (e.g., Raspberry Pis, old laptops, smartphones) into high-efficiency AI inference clusters via distributed computing. This drastically lowers the barrier to running billion-parameter models.

Why Distributed LLMs?
Traditional large language models (e.g., Llama, DeepSeek) rely heavily on high-end GPUs (e.g., NVIDIA A100/H100), which are costly and energy-inefficient. Distributed LLMs, however, leverage dynamic model slicing and cross-device collaborative computing to distribute compute demands across multiple devices, enabling:

Low cost: Replace expensive GPUs with “scrap” compute from idle devices.
Scalability: Linearly boost inference speed by adding nodes.
Cross-platform compatibility: Mix ARM (Raspberry Pi) and x86 devices in a single network.

Core Breakthroughs
Since its launch in 2024, the project has deployed multiple open-source LLMs on clusters of Raspberry Pi 5, Macs, and PCs using Tensor parallelism and Q80 quantization.

DeepSeek R1

Technical Deep Dive

Dynamic Model Slicing
- Auto-load balancing: Splits models into independent compute units based on device count (requires 2ⁿ nodes).
- Raspberry Pi optimizations: ARM-specific operator optimizations increase CPU utilization by 40%.
- Memory compression: Q80 quantization reduces per-node memory usage to 2.4GB (from 6.32GB).
Efficient Communication Protocol
- Low-latency sync: <60ms KV Cache sync delay over Gigabit Ethernet.
- Fault tolerance: Auto-redistributes tasks if any node drops offline.
Cooling Solution
- Add Pi5 cooling fans to reduce full-load temperatures by 15°C.